561 results about "Completion time" patented technology

The invention provides a resource scheduling control method and system for a big data server, and the method comprises the steps: constructing a multi-dimensional resource portrait module, collecting the CPU, memory, network, storage I / O load and task queue length of each node in real time, and predicting a resource demand trend through a time sequence algorithm; extracting characteristics such as calculation intensity, data dependence, memory requirements, network transmission quantity and the like; adjusting the weight coefficients of the resource utilization rate, the task completion time and the energy consumption efficiency according to the system load and the historical effect; establishing a bipartite graph model by taking a resource trend as a node feature and a task vector as an edge feature, and calculating a matching score through graph convolution and a multi-objective optimization function; the scheduling scheme is synchronized by adopting a consistency algorithm; automatic rollback and reallocation are carried out when resources are detected to be insufficient; and optimizing a weight coefficient and a network parameter through reinforcement learning. Through the method, the system resource utilization rate can be improved, the task execution efficiency is improved, the overall scheduling effect stability is improved, and the system fault recovery time is shortened.

The invention discloses an AI-based intelligent factory dynamic production scheduling optimization method and system, and belongs to the technical field of factory dynamic production scheduling, and the method comprises the following steps: obtaining and integrating the working states of various types of equipment in a factory, the stock position states of different materials, and the manual data; the production order state, the process route requirement, the plan and the completion time of each process and the delivery date requirement of the customer are determined; generating an initial production scheduling plan through an AI optimization algorithm in combination with supply chain material data, factory storage space, equipment switching cost, production rules, each order process dependency relationship and a production target; according to the method, the initial scheduling plan is generated by acquiring and integrating multiple types of production data, the dynamic events are monitored, multiple schemes are generated through evaluation, the optimal scheme is selected through economic model evaluation, and production refinement, dynamic response and benefit optimization are achieved.

The invention relates to the technical field of engineering construction progress intelligent control, in particular to an engineering construction progress intelligent control method and system based on digital twinning, and the method comprises the steps: obtaining a preset plan logic relation set; acquiring a real-time process state set of the construction site; determining a plan failure index through real-time logic deviation calculation; in response to the plan failure index being greater than a preset failure determination threshold, determining that the plan fails and switching to a logic emergence mode; in response to the plan failure index being less than or equal to the failure determination threshold, maintaining the plan driving mode; deducing and generating an emergence logic set through emergence logic confidence calculation; determining a failure logic set; reconstructing to generate a hybrid logic model; the prediction completion time is deduced again; outputting a decision support signal; according to the method, the defect that a traditional system cannot judge the failure is overcome, the prediction accuracy is improved, and invalid warning out of reality is avoided.

The invention provides a deadline constraint-based DAG application dynamic scheduling optimization method and system, and the method comprises the steps: converting an application deadline into an instant reward of each task scheduling through employing a DAG structured encoder, a Transform encoding network based on gating feature fusion, and a multi-action selection deep reinforcement learning task scheduling method based on a pointer network, in combination with a dynamic mask scheme, the mobility of a DAG application and the dynamic nature of edge resources are dealt with, then priority subtask selection and real-time decision of the scheduling position of the priority subtask selection are made, and the completion time and execution energy consumption of the application are reduced. In order to stabilize and accelerate DRL scheduler training, task encoder training and reinforcement learning training are decoupled, and a DAG encoder is pre-trained based on self-supervised learning.

The invention relates to a dual-resource constraint flexible job shop scheduling method for reducing worker load, which comprises the following steps: S1, construction of a worker load model: dividing the worker load model into four conditions of light work, moderate work, micro-severe work and rest according to daily work arrangement of workers, and setting the maximum working time length for each worker, the calculation module is used for calculating extra workloads; in the scheduling method provided by the invention, three different initialization strategies are combined, and the proportion of the initialization strategies in a population is set, so that the diversity and quality of an initial solution are ensured, specifically, the used initialization strategies comprise random initialization, initialization according to the process completion time and initialization according to the process remaining time; under the random initialization strategy, the initial solution of the population has great diversity, which is helpful for avoiding the trouble of a local optimal solution.

The invention discloses a manufacturing task autonomous negotiation and execution method based on a large language model agent, and the method comprises the steps: constructing a production scheduling agent, an equipment management agent, a material distribution agent and a quality control agent, analyzing a natural language task instruction through the production scheduling agent, and decomposing the natural language task instruction into subtasks; each agent calculates a utility value based on the load rate, the resource matching degree, the estimated completion time and the historical success rate, and performs structured negotiation to achieve a task allocation consensus; a prediction-check-rollback architecture is adopted to generate an action instruction sequence, the sequence is compiled into a time Petri network transition sequence, and reachability verification is carried out based on hard security constraints; production environment data is collected in real time to trigger anomaly detection and re-negotiation, and a formalized security verification and causal anti-factual reasoning parameter updating mechanism is introduced. According to the method, unstructured instruction understanding, autonomous task planning, multi-agent collaborative decision and closed-loop optimization are realized, and the problems of real-time performance, safety and interpretability of a large language model in manufacturing control are solved.

The invention discloses a construction management system and method based on multi-source data analysis, and relates to the technical field of building construction informatization and intelligent management and control, and the method comprises the steps: capturing a front path task completion signal and a subsequent task starting instruction; obtaining preposed task completion time, a dynamic condition type and a threshold parameter; executing time logic verification based on the dynamic condition type, and judging a construction dynamic context ready state; bIM design coordinates, field positioning coordinates and space tolerance parameters of the construction machinery are obtained, and space logic verification is executed by calculating space deviation; when both the time verification and the space verification pass, subsequent task state conversion is allowed, and otherwise, blocking is carried out; and finally generating a structured report containing all verification parameters and results.

The invention relates to the technical field of time sequence data processing and deep learning, in particular to a time sequence filling method and system based on a coarse-to-fine filling normal form. In the invention, a preprocessing module is used for sampling an input sequence to obtain a subsequence rk with the length of Lk, and a regression prediction module is combined with a causal mask to generate a prediction value r'k with the same structure and the same length as the rk based on all subsequences {r1... rk} generated by the preprocessing module; the correction module performs up-sampling on the predicted value r'k to obtain an up-sampling sequence r ''k with the length of T; supplementing missing data of the original sequence based on the r ''k to obtain a corrected sequence X (k + 1); and traversing k = 1... K to obtain a correction sequence X (K + 1) as a final repair completion time sequence. The method overcomes the defects that the time sequence filling mode in the prior art does not consider the unfixed missing rate and missing value block distribution, and is beneficial to improving the filling accuracy.

The invention relates to the technical field of flexible job shop scheduling in intelligent manufacturing and production scheduling, in particular to a flexible job shop scheduling optimization method for solving and adjusting resource constraints. Comprising the steps of initializing parameters and randomly generating an initial population; sequentially using a crossover operator and a mutation operator to evolve the current population; executing a hybrid decoding strategy on the current population; sorting the individuals in the current population from small to large according to the maximum completion time to form an elite population, and updating the elite population through question specific local search; and judging whether an evolution condition is met or not, if so, executing CP-based mathematical evolution, and outputting a final solution when the running time reaches the total running time. The method has the positive effects of reducing the resource waiting time, improving the machine utilization rate and improving the resource utilization efficiency and the scheduling performance of the whole workshop production.

The invention discloses an underwater acoustic processing CPU-GPU dynamic load balancing method based on a task flow model, and belongs to the technical field of underwater acoustic processing. The method comprises the following steps: deconstructing an underwater acoustic processing application into a task flow model represented by a directed acyclic graph; establishing a feature portrait including calculation complexity, parallelism and data throughput for each task node, and constructing a cost prediction model; the CPU / GPU utilization rate and the data transmission performance are monitored in real time; a processor is distributed to each task node by using a dynamic programming algorithm in combination with a cost prediction model and a real-time system state with the goal of minimizing the total task flow completion time; and dynamically scheduling tasks through a central scheduler according to a decision result, and periodically updating a strategy. According to the method, the defects that static task division lacks adaptability and neglects task dependence and communication overhead are overcome, dynamic and efficient utilization of CPU and GPU resources is achieved, and the efficiency and real-time performance of underwater sound processing are remarkably improved.

According to the motion planning method and device for the multi-mechanical-arm cooperative task, a large number of potential conflicts are actively avoided in the early stage of task allocation through division of a virtual wall and a safety area and a danger area, the difficulty and the calculated amount of follow-up track collaboration are remarkably reduced, and the success rate and the efficiency of planning are improved. Cost and load balance are considered in the task allocation stage; in the path point sorting stage, minimizing the total movement distance and the total completion time is taken as a target; and track generation adopts a time optimal algorithm. The layered decoupling design avoids the huge calculation overhead of centralized planning, and meanwhile, the quality of a final solution is ensured through the optimization strategy of each stage. The innovative cost function takes collision distance into consideration, and guides the planner to select a safer path. Through layered and ordered collision solution strategies, such as deceleration, local re-planning and optimal waiting, it is ensured that a collision-free solution can be always found in a complex dynamic environment, and robustness is high.

The invention discloses a laboratory sample detection management method, system, equipment and medium based on automatic scheduling, and belongs to the technical field of laboratory intelligent scheduling, and the method comprises the following steps: collecting task and equipment data in real time, binding and storing, and quantifying an initial priority by calculating task complexity; the priority is dynamically adjusted by combining the fault severity, queuing duration attenuation and environment temperature and humidity overrun correction, the completion time is predicted based on the historical efficiency of the equipment and the current load, the task is preferentially allocated to the automatic equipment for execution according to the corrected priority score, and the progress is updated in real time. The intelligent scheduling capability, the resource utilization efficiency and the operation stability of laboratory detection tasks are improved, and efficient and reliable technical support is provided for a complex and changeable experiment environment.

The invention discloses a reconfigurable flexible job shop scheduling optimization method with secondary clamping constraint, and relates to the technical field of intelligent manufacturing and production optimization. The method comprises the following steps of: 1) establishing a mixed integer linear programming model considering a reconfigurable flexible job shop scheduling problem of secondary clamping by taking minimization of maximum completion time and minimum number of chemical workers as targets; 2) designing a three-segment coding mode and a decoding mode corresponding to the mixed integer linear programming model based on process sorting, machine selection and worker selection; and 3) based on the three-segment coding mode and the decoding mode, adopting an improved multi-target genetic algorithm to solve an optimal scheduling scheme of the mixed integer linear programming model. According to the method, processing machine selection, auxiliary module selection, processing sequence sorting and secondary clamping worker selection of a manufacturing workshop can be considered at the same time, the workshop production efficiency is improved, and the method has the advantages of being good in model performance, small in result fluctuation and high in stability.

The invention relates to the field of precise motion control, in particular to a precise motion control method and system for a multi-axis linkage direct-drive-force tool tower crane. Comprising the following steps: acquiring a processing track, dispersing the processing track into a plurality of spatial motion units, and determining a target displacement for each motion axis; receiving a completion signal; and after determining that the motion axis participating in the current space motion unit completes the corresponding target displacement according to the completion signal, obtaining the completion time of each motion axis, and issuing the target displacement corresponding to the next space motion unit to each motion axis by taking the completion time of the motion axis with the longest completion time as a time reference. Therefore, each motion shaft is started to move the next space motion unit. The technical problems that the machining precision is reduced and the product quality is unqualified due to clock frequency drift and movement shaft synchronization deviation accumulation caused by gradual change of microcosmic physical characteristics in a system of a multi-shaft linkage direct-driving-force tool tower crane are solved.

The invention discloses a workshop dynamic scheduling method considering equipment occupation under emergency order insertion, which comprises the following steps: receiving a trigger event in a production system, and obtaining workshop production real-time state data according to the trigger event; according to the real-time state data, a dynamic scheduling decision engine is activated, and a global scheduling problem is decomposed into deterministic sub-problems at the current decision moment; according to the deterministic sub-problem, identifying the state of a work-in-process and generating an unfinished process set, and re-bringing the unfinished process set into a rearrangement resource pool; selecting a rearrangement strategy based on a preset influence threshold according to the unfinished process set; according to the rearrangement strategy, a planning model is constructed, and the planning model aims at minimizing the maximum completion time and minimizing the order insertion change degree; according to the planning model, an improved genetic simulated annealing algorithm is adopted for solving, and a dynamic scheduling scheme is output and issued to a production execution layer. According to the invention, the disturbance of order insertion on the original production system can be minimized.

The invention designs a workshop dynamic scheduling method based on an improved genetic algorithm and multi-objective optimization. According to the method, in order to solve the problem that a scheduling scheme fails after dynamic events such as equipment failure, order insertion or material delay occur in a discrete manufacturing workshop, a greedy strategy is adopted for pre-scheduling, and rapid rescheduling is performed based on an improved genetic algorithm after the dynamic events occur. The algorithm improves search efficiency and scheduling stability through multi-population parallel evolution, differential evolution self-adaptive parameter adjustment and an elitist retention mechanism. And taking minimization of the maximum completion time, the total delay time and the equipment change frequency as multiple targets, and obtaining a comprehensive optimal solution through a weighted summation method. According to the method, the scheduling response speed and stability of the workshop in a dynamic environment can be remarkably improved, and the workshop production efficiency and the equipment utilization rate are improved.

The invention discloses a production line unit task allocation method based on reinforcement learning, which comprises the following steps: extracting a specific task instruction of each production line unit from a final task allocation matrix, verifying the feasibility of the instruction under the constraint of completion time through a simulation execution module, determining an instruction set passing verification, and performing task allocation on the instruction set; wherein the instruction set is obtained by fusing the solution of the multi-target conflict; according to the verified instruction set, production line feedback data such as actual execution time and energy consumption records are obtained, deviation is analyzed from the feedback data, if it is judged that the deviation is larger than a preset threshold value, a self-adaptive adjustment mechanism is triggered, and a corrected distribution strategy is obtained; and the optimized resource allocation indexes are extracted from the corrected allocation strategy, the indexes are pushed to the production line equipment through the real-time distribution system, the execution monitoring cycle after pushing is determined, and the monitoring cycle is obtained by continuously tracking the low-efficiency risk.

The invention relates to the technical field of project management intelligent decision making, in particular to a project management intelligent decision making system integrating multi-target resource scheduling optimization. The system comprises an equipment state sensing module, an environment monitoring module, a data acquisition module, a conflict evaluation module and a dynamic decision module. The equipment state sensing module is used for collecting operation parameters of key construction equipment in real time, wherein the operation parameters comprise equipment real-time position coordinates, actual speed, fault early warning coefficients, actual working time and actual completion time. The data acquisition module is used for acquiring supply chain data and construction plan data. The equipment state sensing module, the data acquisition module and the environment monitoring module are arranged for cooperative use, so that the system can automatically sense environment changes such as wind speed and wind direction, the equipment offset risk is calculated through the conflict evaluation module in real time, and meanwhile, the resource scheduling instruction is generated, so that the error correction response time is greatly shortened, and the error correction efficiency is improved. And meanwhile, the delay time of the construction period is reduced.

The invention relates to the technical field of cloud computing and distributed computing scheduling, in particular to a server-free MapReduce job scheduling optimization method. Comprising the following steps: initializing Bayesian genetic algorithm operation parameters; forming an initial population; the current population executes variable neighborhood search to generate a new solution, and the population is updated; constructing a Bayesian probability model; generating a new solution through Bayesian probability sampling and genetic manipulation, and updating the population; updating a global optimal solution; and judging whether the time limit is reached or not, if so, outputting a globally optimal solution and the corresponding maximum completion time, and if not, continuing iteration. The application of the method has the positive effects of minimizing the maximum completion time of the operation and improving the robustness of the algorithm and the scheduling efficiency.

The invention relates to the technical field of computer science and industrial engineering, in particular to a tool workshop scheduling method, which comprises the following steps of: acquiring production characteristics of a current tool workshop, generating an optimal batch scheduling strategy according to the production characteristics of the current tool workshop based on a preset evolutionary strategy, and automatically designing an algorithm by utilizing a preset large model, and calculating the minimum completion time and the maximum completion time of the optimal batch scheduling strategy, and completing tool workshop scheduling according to the minimum completion time and the maximum completion time based on the optimal batch scheduling strategy. Therefore, the problems of high workshop scheduling complexity and the like caused by strong coupling of batch division and working procedure sorting, strict constraint between working procedures and high resource networking degree in workshop scheduling are solved, an automatic algorithm design framework based on a large model is introduced, the expert algorithm design time is shortened, and the workshop scheduling efficiency is improved. Therefore, the efficiency and feasibility of aircraft tooling production scheduling are improved.

The invention relates to the technical field of furniture production management, and discloses an intelligent management method and system for furniture production. The method comprises the following steps: collecting production data such as material consumption, equipment operation parameters and process completion time of each process node on a production line in real time; performing multi-dimensional feature extraction on the data to generate a comprehensive feature set containing time sequence, statistical and associated features; dividing continuous production stages according to a characteristic dynamic change rule and distributing identifiers; based on the identifier recombination data, calculating a transition probability matrix between adjacent stages; identifying a potential abnormal stage and generating a mark sequence by analyzing a state jump abnormal mode in the matrix; constructing a quality prediction model in combination with the abnormal mark and the real-time data, and outputting a quality prediction score of each process node; dynamically adjusting a procedure production parameter configuration scheme according to the deviation degree of the score and a preset threshold value; and performing similarity matching on the adjusted scheme and historical optimal configuration, and screening a to-be-verified configuration set to perform a simulation test.

The invention belongs to the field of workshop scheduling, and particularly discloses a hybrid flow workshop dynamic scheduling method and system considering machine predictive maintenance, and the method comprises the steps: taking the minimization of total completion time, maintenance cost and processing cost as the target, building a hybrid flow workshop scheduling problem as a multi-target joint optimization model, and carrying out the optimization of the multi-target joint optimization model; setting intelligent agents with the same number as the processing stages, and constructing a Markov decision process; each agent has an independent scheduling network, including a workpiece scheduling network and a machine scheduling network; based on a Markov decision process, training an intelligent agent, maintaining a workshop at an operation and maintenance point, and respectively calling a workpiece scheduling network and a machine scheduling network at a scheduling point to select workpieces and machines; and after training is completed, workshop dynamic scheduling is realized based on the trained intelligent agent. According to the method, the problem of hybrid flow shop dynamic scheduling considering machine predictive maintenance is effectively solved through workshop scheduling integrating machine operation and maintenance, and the method has good dynamics and adaptivity.

The invention discloses a multi-objective reinforcement learning man-machine cooperative assembly task allocation method based on neighborhood parameter migration, and the method comprises the steps: building a mathematical model which aims at minimizing the physiological fatigue accumulated value of a human operator and minimizing the maximum completion time for a multi-objective optimization problem of task allocation in a man-machine cooperative assembly system; a multi-target problem is decomposed into N standard sub-problems by adopting a weighting and decomposition strategy, and training is accelerated through a neighborhood parameter migration strategy; each sub-problem is solved based on a near-end strategy optimization algorithm of an Actor-Critic framework, Gaussian noise is added to an Actor network to simulate environment uncertainty, an action mask mechanism is introduced to process priority constraints of assembly tasks, and it is ensured that a generated task allocation scheme is always feasible. According to the method, the convergence speed and diversity of the Pareto solution set can be remarkably improved, and the assembly efficiency and operator fatigue are effectively balanced.

The invention discloses a cloud computing task scheduling method, device and system based on deep reinforcement learning, and the method comprises the steps: carrying out the preprocessing of a task, and obtaining the priority of the task; the task with the highest priority is selected according to the priorities of the tasks, partition selection is converted into a Markov decision process, the Markov decision process is executed through a first D3QN agent, and the tasks are distributed to corresponding virtual machine partitions; and converting task scheduling into a Markov decision process, and executing the Markov decision process through the second D3QN intelligent agent to distribute the tasks to the corresponding virtual machines so as to realize task scheduling. A task scheduling model is designed, task scheduling is regarded as a packing problem by the model, and resource requirements of tasks and execution capability of heterogeneous virtual machines are considered at the same time. Meanwhile, reduction of task completion time and maintenance of load balance of a cluster environment are taken as optimization objectives, so that multi-objective optimization of task scheduling is realized.

The invention discloses a service scheduling method and system for a photoelectric hybrid low earth orbit satellite network, and relates to the technical field of satellite communication and network resource management.The method comprises the steps that the photoelectric hybrid low earth orbit satellite network is constructed, and topological information, node information and a to-be-scheduled service set of the photoelectric hybrid low earth orbit satellite network are obtained; establishing a mixed integer linear programming model containing node selection constraint, service scheduling sequence constraint and routing constraint by taking the minimum weighted sum of the total service completion energy consumption and the total service completion time as a target; in order to solve the problem of high model complexity, a heuristic algorithm based on simulated annealing is designed, and efficient solution is carried out by iteratively optimizing a scheduling sequence and a routing path of a service; and finally, implementing service scheduling according to the obtained optimal scheduling scheme. According to the method, heterogeneous characteristics of the photoelectric nodes and link resource conflicts are fully considered, dynamic balance of energy consumption and time delay is achieved, network energy efficiency and business service quality are remarkably improved, and the method is suitable for efficient operation of large-scale low-orbit satellite constellations.

The invention belongs to the technical field of flow shop scheduling, and particularly relates to a hybrid flow shop full-active batch scheduling method. On the basis of a construction process of full active scheduling, complete scheduling is generated, and a compact and feasible initial solution is obtained; a damage-repair process based on a track is introduced, and complete scheduling is enhanced by focusing key operation to gradually converge to a stable state. According to the method, for the first time, on the basis of a construction process of full-active scheduling, complete scheduling is generated, batch dispatching, batching and distribution rules are effectively integrated, a high-quality scheme is constructed by utilizing a weighted priority mechanism, a damage-repair process based on a track is introduced, and the complete scheduling is strengthened by focusing key operation and is gradually converged to a stable state. Cross-stage waiting can be reduced, the equipment utilization rate is improved, and the total completion time is shortened; a high-quality schedule is generated in a short calculation time, and the method is suitable for a real-time or quasi-real-time production environment.

Methods, systems, and devices for adaptive memory status reporting are described. For example, the memory system may store one or more status indicators to a register for a host system to access. The status indicators may represent an estimated completion time for a quantity of access commands in a command queue of the memory system. The host system may use the status indicators to detect an unresponsive memory system. For example, the host system may query the register to determine whether to continue to wait for the commands to complete or to issue an abort command. Additionally, or alternatively, the host system may initiate memory management operations to assist the memory system in completing executing the commands.

The invention discloses a green robust independent parallel locomotive scheduling method with uncertain processing time. The method comprises the following steps: acquiring a to-be-scheduled parameter set; constructing an irrelevant parallel machine scheduling model taking worst scene completion time WC and scene average energy consumption MTEC as double targets based on the parameters; a scene-driven double-population discrete artificial bee colony algorithm is adopted for solving, and the method comprises the steps of population initialization, employed bee global search based on ternary championics and two-point crossing, division into two sub-populations according to MTEC, MN local search based on a mean value scene, WN local search based on a worst scene, LN observation bee self-adaptive neighborhood search based on Q-learning and scout bee disturbance. And finally, outputting a robust scheduling solution set with both robustness and low-carbon property according to a Pareto criterion. And a plurality of scheduling schemes considering robustness and energy consumption optimization are provided for decision makers.

The invention discloses a space-time cooperative scheduling method for an intelligent sky rail and an AGV in an automatic container terminal, and the method comprises the steps: determining the scheduling constraint conditions of an SMV and the AGV based on an SMV and AGV dual-cycle strategy, and constructing a model and constraint conditions which take the minimization of the completion time of all tasks as a target; using an LBBD algorithm to decompose the mixed integer linear programming model into a main problem and a sub-problem, constructing three acceleration strategies based on SMV and AGV dual-cycle strategies, embedding acceleration cut into the main problem as a constraint condition, solving the main problem and the sub-problem under the constraint condition, and generating Benders cut; embedding the Benders into the main problem, and solving again to obtain a scheduling optimization result; the scheduling decision quality is fundamentally improved, a set of scientific and efficient SMV-AGV collaborative operation method is provided for an intelligent air rail system, the equipment utilization rate can be remarkably improved, the operation completion time can be shortened, the optimal collaborative scheduling scheme can be rapidly and accurately obtained in a large-scale task scene, and the unloaded driving cost, the energy consumption cost and the operation cost are synchronously reduced.

The invention relates to a flow control method and device based on a dynamic queue weight and a global overflow bucket. The method comprises the following steps: distributing network messages to corresponding message queues according to a preset classification rule; dynamically generating the queue weight of each queue according to the real-time queue state of each queue; calculating virtual completion time of each queue based on the queue weight; determining a target message to be sent according to the virtual completion time; when the number of the tokens in the queue where the target message is located is insufficient, the tokens are called from a global overflow bucket; and sending the target message based on the called token. According to the flow control method and device based on the dynamic queue weight and the global overflow bucket, time delay can be reduced, packet loss can be reduced, the utilization rate of network resources can be improved, burst flow processing can be optimized, real-time service experience can be guaranteed, token waste can be avoided, and queue bandwidth allocation can be balanced.